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超级传播事件的传染外部性:斯特吉斯摩托车拉力赛与新冠疫情

The contagion externality of a superspreading event: The Sturgis Motorcycle Rally and COVID-19.

作者信息

Dave Dhaval, McNichols Drew, Sabia Joseph J

机构信息

Bentley University Waltham Massachusetts USA.

IZA Bonn Germany.

出版信息

South Econ J. 2021 Jan;87(3):769-807. doi: 10.1002/soej.12475. Epub 2020 Dec 2.

Abstract

Large in-person gatherings of travelers who do not socially distance are classified as the "highest risk" for COVID-19 spread by the Centers for Disease Control and Prevention (CDC). From August 7-16, 2020, nearly 500,000 motorcycle enthusiasts converged on Sturgis, South Dakota for its annual rally in an environment without mask-wearing requirements or other mitigating policies. This study is the first to explore this event's public health impacts. First, using anonymized cell phone data, we document that foot traffic at restaurants/bars, retail establishments, and entertainment venues rose substantially at event locations. Stay-at-home behavior among local residents fell. Second, using a synthetic control approach, we find that the COVID-19 case rate increased substantially in Meade County and in the state of South Dakota in the month following the Rally. Finally, using a difference-in-differences model to assess nationwide spread, we find that following the Sturgis event, counties outside of South Dakota that contributed the highest inflows of rally attendees experienced a 6.4-12.5% increase in COVID-19 cases relative to counties without inflows. Our findings highlight that local policy decisions assessing the tradeoff between local economic benefits and COVID-19 health costs will not be socially optimal in the presence of large contagion externalities.

摘要

美国疾病控制与预防中心(CDC)将未保持社交距离的大量旅行者聚集活动列为新冠病毒传播的“最高风险”。2020年8月7日至16日,近50万名摩托车爱好者齐聚南达科他州斯特吉斯参加年度集会,集会环境没有佩戴口罩要求或其他缓解政策。本研究首次探讨了该活动对公共卫生的影响。首先,我们使用匿名手机数据记录了集会地点的餐厅/酒吧、零售场所和娱乐场所的人流量大幅上升。当地居民的居家行为减少。其次,我们采用合成控制法发现,集会后的一个月里,米德县和南达科他州的新冠病例率大幅上升。最后,我们使用差分模型评估全国范围的传播情况,发现斯特吉斯集会活动后,集会参与者流入量最高的南达科他州以外的县,其新冠病例数相对于没有流入的县增加了6.4%-12.5%。我们的研究结果表明,在存在巨大传染外部性的情况下,评估地方经济利益与新冠疫情健康成本之间权衡的地方政策决策并非社会最优。

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